Missing data filling method based on Aitchison simplex space

2021 ◽  
Author(s):  
Bing Liu
Keyword(s):  
Missing Data ◽  
Filling Method ◽  
Simplex Space

scholarly journals Data Analysis Approach for Incomplete Interval-Valued Intuitionistic Fuzzy Soft Sets

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1061
Author(s):  
Hongwu Qin ◽  
Huifang Li ◽  
Xiuqin Ma ◽  
Zhangyun Gong ◽  
Yuntao Cheng ◽  
...  
Keyword(s):  
Missing Data ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Intuitionistic Fuzzy ◽  
Degree Of Membership ◽  
Filling Method ◽  
Fuzzy Soft Sets ◽  
Interval Valued ◽  
Intuitionistic Fuzzy Soft Sets

The model of interval-valued intuitionistic fuzzy soft sets is a novel excellent solution which can manage the uncertainty and fuzziness of data. However, when we apply this model into practical applications, it is an indisputable fact that there are some missing data in many cases for a variety of reasons. For the purpose of handling this problem, this paper presents new data processing approaches for an incomplete interval-valued intuitionistic fuzzy soft set. The missing data will be ignored if percentages of missing degree of membership and nonmember ship in total degree of membership and nonmember ship for both the related parameter and object are below the threshold values; otherwise, it will be filled. The proposed filling method fully considers and employs the characteristics of the interval-valued intuitionistic fuzzy soft set itself. A case is shown in order to display the proposed method. From the results of experiments on all thirty randomly generated datasets, we can discover that the overall accuracy rate is up to 80.1% by our filling method. Finally, we give one real-life application to illustrate our proposed method.

scholarly journals The missing data filling method of the industrial internet platform based on rules and lightGBM

2020 ◽  
Vol 53 (5) ◽  
pp. 152-157
Author(s):  
Qingmin Yu ◽  
Xin Guan ◽  
Yong Zhai ◽  
Zhaoteng Meng
Keyword(s):  
Missing Data ◽  
Industrial Internet ◽  
Filling Method ◽  
Internet Platform

scholarly journals Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

2021 ◽  
Vol 14 (1) ◽  
pp. 172
Author(s):  
Zhipeng Tang ◽  
Giuseppe Amatulli ◽  
Petri K. E. Pellikka ◽  
Janne Heiskanen
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Land Cover ◽  
Temporal Information ◽  
Data Reconstruction ◽  
Large Area ◽  
Landsat Images ◽  
Accurate Performance ◽  
Filling Method ◽  
One Year

The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.

MISSING DATA

1979 ◽  
Vol 24 (8) ◽  
pp. 670-670
Author(s):  
FRANZ R. EPTING ◽  
ALVIN W. LANDFIELD
Keyword(s):  
Missing Data

MORE MISSING DATA

1979 ◽  
Vol 24 (12) ◽  
pp. 1058-1058
Author(s):  
AL LANDFIELD ◽  
FRANZ EPTING
Keyword(s):  
Missing Data

Impact of missing data in a weight control trial

2013 ◽  
Author(s):  
Samantha Minski ◽  
Kristen Medina ◽  
Danielle Lespinasse ◽  
Stacey Maurer ◽  
Manal Alabduljabbar ◽  
...  
Keyword(s):  
Missing Data ◽  
Weight Control
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